At the Strata + Hadoop World show in New York City today, Hadoop distribution specialist MapR Technologies announced the addition of native JSON support to its MapR-DB NoSQL database, packaged with its MapR Distribution including Hadoop.

"This is a significant step forward," Norris says. "This provides the ability to build scalable JSON apps, allowing developers to leverage the many different JSON sources that are rapidly expanding."

Norris notes the addition of an in-Hadoop document database to the distribution allows developers to benefit from the advantages of a document database combined with the scale, reliability and integrated analytics of enterprise-grade Hadoop and Spark. That, in turn, will allow them to improve a range of use cases, from personalizing online shopping experiences to reducing risk and preventing fraud in real-time, to improving manufacturing efficiencies and lowering costs.

MapR-DB supports the Open JSON Application Interface (OJAI), a general purpose JSON access layer across databases, file systems and message streams. The new capabilities are designed to give developers the capability to accomplish the following:

Build and deploy scalable JSON-based applications, shortening application development cycles and avoiding complex persistence methods, with the power to handle production workloads and global deployments

Run continuous analytics, allowing for the development of rich applications that leverage large-scale, real-time analytics

Gain faster time-to-value by quickly standing up more business applications on more data types and sources using familiar JSON patterns

"MapR continues to build on the innovative data platform at the core of its Hadoop distribution," Nik Rouda, senior analyst, Enterprise Strategy Group, said in a statement topday. "The addition of a document database capability (JSON) neatly extends the powerful NoSQL MapR-DB to seamlessly cover more types of unstructured business data. This makes it faster and easier to build big data applications, without the burden of shuffling data around first."

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